Visual gait recognition, which aims to identify a target person using her/his walking pattern in a video, has been studied for over two decades [1]}, [2]}.
Existing approaches and datasets are dominated by 2D gait representations such as silhouette sequences [3]}, Gait Energy Images (GEIs) [4]}, 2D skeletons [5]}, as shown in Figure REF .
However, the human body is a 3D non-rigid object, so the 3D-to-2D projection discards a lot of useful information about shapes, viewpoints, and dynamics while presenting ambiguity for gait recognition.
Therefore, this paper is focused on 3D gait recognition which is valuable yet neglected by the community.
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